SCMCP

Instructions

Implementation Reference

• src/scmcp/tool/ccc.py:75-89 (handler)

  The handler function that executes the ccc_dot_plot tool logic. It prepares filtered kwargs and calls liana.pl.dotplot to generate the dotplot visualization.

  def plot_dotplot(adata, **kwargs):
      pval = kwargs.pop("specificity_cutoff", 0.05)
      res_key = kwargs.get("uns_key", "liana_res")
      pval_col = adata.uns[res_key].columns[-1]
      kwargs["filter_fun"] = lambda x: x[pval_col] <= pval
  
      if kwargs.get("colour", None) is None:
          kwargs["colour"] = adata.uns[res_key].columns[-2]
      if kwargs.get("size", None) is None:
          kwargs["size"] = adata.uns[res_key].columns[-1]        
  
      parameters = inspect.signature(li.pl.dotplot).parameters
      kwargs = {k: kwargs.get(k) for k in parameters if k in kwargs}    
      fig = li.pl.dotplot(adata, **kwargs)
      return fig

• src/scmcp/schema/ccc.py:315-408 (schema)

  Pydantic model defining the input schema for the ccc_dot_plot tool, including parameters like uns_key, specificity_cutoff, colour, size, etc.

  class DotPlotModel(JSONParsingModel):
      """Input schema for LIANA's dotplot visualization for cell-cell communication networks."""
      
      uns_key: str = Field(
          default="liana_res",
          description="Key in adata.uns that contains the LIANA results."
      )
      
      specificity_cutoff: float = Field(
          default=0.05,
          description="specificity or pval threshold for filtering results. "
      )
          
      colour: Optional[str] = Field(
          default=None,
          description="Column in liana_res to define the colours of the dots."
      )
      
      size: Optional[str] = Field(
          default=None,
          description="Column in liana_res to define the size of the dots."
      )
      
      source_labels: Optional[Union[List[str], str]] = Field(
          default=None,
          description="List of labels to use as source, the rest are filtered out."
      )
      
      target_labels: Optional[Union[List[str], str]] = Field(
          default=None,
          description="List of labels to use as target, the rest are filtered out."
      )
      
      top_n: Optional[int] = Field(
          default=None,
          description="Top N entities to plot."
      )
      
      orderby: Optional[str] = Field(
          default=None,
          description="If top_n is not None, order the interactions by this column."
      )
      
      orderby_ascending: Optional[bool] = Field(
          default=None,
          description="If top_n is not None, specify how to order the interactions."
      )
      
      orderby_absolute: bool = Field(
          default=False,
          description="If top_n is not None, whether to order by the absolute value of the orderby column."
      )
      
      ligand_complex: Optional[Union[List[str], str]] = Field(
          default=None,
          description="List of ligand complexes to filter the interactions to be plotted."
      )
      
      receptor_complex: Optional[Union[List[str], str]] = Field(
          default=None,
          description="List of receptor complexes to filter the interactions to be plotted."
      )
      
      inverse_colour: bool = Field(
          default=False,
          description="Whether to -log10 the colour column for plotting."
      )
      
      inverse_size: bool = Field(
          default=False,
          description="Whether to -log10 the size column for plotting."
      )
      
      cmap: str = Field(
          default="viridis",
          description="Colour map to use for plotting."
      )
      
      size_range: Tuple[int, int] = Field(
          default=(2, 9),
          description="Define size range. Tuple of (min, max) integers."
      )
      
      figure_size: Tuple[float, float] = Field(
          default=(8, 6),
          description="Figure x,y size."
      )
      
      specificity_cutoff: float = Field(
          default=0.05,
          description="Specificity or p-value threshold for filtering results."
      )

• src/scmcp/tool/ccc.py:36-40 (registration)

  Definition of the MCP Tool object for ccc_dot_plot, including name, description, and input schema.

  dot_plot_tool = types.Tool(
      name="ccc_dot_plot",
      description="Visualize cell-cell communication interactions using a dotplot.",
      inputSchema=DotPlotModel.model_json_schema(),
  )

• src/scmcp/tool/ccc.py:100-106 (registration)

  ccc_tools dictionary that maps tool names to Tool objects, including ccc_dot_plot.

  ccc_tools = {
      "ls_ccc_method": ls_ccc_method_tool,
      "ccc_rank_aggregate": rank_aggregate_tool,
      "ccc_circle_plot": circle_plot_tool,
      "ccc_dot_plot": dot_plot_tool,
      "ccc": ccc_tool,
  }

• src/scmcp/server.py:48-55 (registration)

  In list_tools(), the ccc_tools are included in the list of available tools returned by the MCP server.

  tools = [
      *io_tools.values(),
      *pp_tools.values(),
      *tl_tools.values(),
      *pl_tools.values(),
      *util_tools.values(),
      *ccc_tools.values(),
  ]